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The back-shifted Fermi gas model is widely employed for calculating nuclearlevel density (NLD) as it can effectively reproduce experimental data byadjusting parameters. However, selecting parameters for nuclei lackingexperimental data poses a challenge. In this study, the feedforward neuralnetwork (FNN) was utilized to learn the level density parameters at neutronseparation energy $a(S_{n})$ and the energy shift $\varDelta$ for 289 nuclei.Simultaneously, parameters for nearly 3000 nuclei are provided through the FNN.Using these parameters, calculations were performed for neutron resonancespacing in $s$ and $p$ waves, cumulative number of levels, and NLD. The FNNresults were also compared with the calculated outcomes of the parameters fromfitting experimental data (local parameters) and those obtained from systematicstudies (global parameters), as well as the experimental data. The resultsindicate that parameters from the FNN achieve performance comparable to localparameters in reproducing experimental data. Moreover, for extrapolated nuclei,parameters from the FNN still offer a robust description of experimental data.

Inference of Parameters for Back-shifted Fermi Gas Model using Feedback Neural Network